Vivek Tiwari
Machine Learning Engineer — GenAI, RAG, and MLOps
vivektiwari
San Francisco, California
Seattle, Washington
Joined December 2025
Network
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Summary
Practitioner of GenAI and production RAG systems — Vivek builds and ships Retrieval-Augmented Generation and agentic GenAI solutions aimed at reliable, production-grade performance. His portfolio and résumé note work on ingestion/ETL for unstructured medical and document text, vector search and Pinecone-based retrieval, LLMOps pipelines, and agent orchestration for real systems. twine+1
Open-source author and technical writer — he publishes medium/TowardsAI articles and maintains GitHub projects that demonstrate applied ML engineering (document intelligence, NdLinear experiments, RL projects). His writing combines implementation details, benchmarking, and reproducible code. medium+2
Focus on document intelligence and structure-aware modeling — several projects (DOCAI and related repositories) and articles emphasize NdLinear and structure-preserving layers for layout-aware transformers, indicating a specialty in form and document understanding. github+1
Experience in healthcare/clinical NLP workflows and productionization — past work at Docplus and IU shows emphasis on building inference APIs, chatbots/multimodal triage, and production ML pipelines with data quality and compliance considerations for medical contexts. wayup+1
Work
Education
Projects
Writing
Revolution in Neural Network Layers: How NdLinear Outperforms Traditional Linear Layers
April 1, 2025A technical article explaining NdLinear (N-dimensional linear layers), comparing nn.Linear vs NdLinear mathematically and empirically, and showing benefits for document intelligence and structured data.
From Pixels to Plans: Cracking 2048 with Reinforcement Learning and Beam Search
March 1, 2025A deep-dive project writeup comparing PPO-based RL agents and deterministic Beam Search for mastering the game 2048, including architecture, results, and code to reproduce experiments.
Hobbies
Writes technical articles and tutorials about machine learning, reinforcement learning, and AI engineering. medium
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